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| Iterative Learning Control with Preset Tracking Time for Nonlinear Systems |
| YIN Chunwu
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| College of Information and Control Engineering, Xi'an University of Architecture and Technology, Xi'an 710055, China |
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Abstract A signal bounded iterative learning control algorithm is designed to address the input saturation constraint and convergence time interval setting problem of second-order nonlinear systems. Using inequality transformation to separate unknown parameter functions in the system, establish a limited amplitude iterative learning algorithm to estimate unknown parameter functions; By means of the predefined time convergence conversion function, the arbitrary initial value sliding mode surface is converted into a new variable with zero initial value, and a variable gain limiting iterative learning controller is constructed. Strict theoretical analysis proves that the sliding mode surface converges to zero after finite iterative learning and all signals of the closed-loop system are uniformly bounded,ensuring the trajectory tracking error converges within the preset time. The numerical simulation of arbitrary initial value robot trajectory tracking control verifies the effectiveness of the proposed method and the excellent characteristics of the convergence time interval that can be set according to engineering requirements.
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Received: 12 June 2024
Published: 14 July 2026
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[1] 马乐乐,刘向杰,高福荣.迭代学习模型预测控制研究现状与挑战[J].自动化学报,2022,48(6):1385-1401.
MA L L, LIU X J, Gao F R. Status and challenges of iterative learning model predictive control[J]. ACTA Automatica Sinica, 2022, 48(6): 1385-1401.
[2] HUANG L, SUN L, WANG T, et al. Optimal input filters for iterative learning control systems with additive noises, random delays, and data dropouts in both channels[J]. Mathematical Methods in the Applied Sciences, 2022(8):45-56.
[3] ZHANG X. Robust iterative learning control design based on iterative integral sliding mode for nonlinear systems with unrepeatable disturbance[J]. Transactions of the Institute of Measurement and Control, 2022, 44(11):2141-2151.
[4] SUN M, ZHAN Y, LI W, et.al. Error tracking approach to discrete-time adaptive iterative learning control against arbitrary initial perturbations[J]. International Journal of Adaptive Control and Signal Processing, 2022(4):36-45.
[5] LIANG C, ZOU Y, CAI C. Robust predictive iterative learning control for linear time‐varying systems[J]. Asian Journal of Control, 2021(9):333-343.
[6] FENG Y, YU G W. Iterative learning control for fractional order nonlinear system with initial shift[J]. Nonlinear Dynamics, 2021, 106(4):3305-3314.
[7] 严求真,孙明轩,蔡建平.含输入死区系统的滤波误差初始修正迭代学习控制[J].控制理论与应用,2017,34(1):77-84.
YAN Q Z,SUN M X,CAI J P. Filtering-error rectified iterative learning control for systems with input dead-zone[J]. Control Theory & Applications, 2017,34(1): 77-84.
[8] 陈建勇,孙明轩.严格反馈系统约束迭代学习控制[J].控制理论与应用,2021,38(5):561-570.
CHEN J Y, SUN M X. Constrained iterative learning control of a class of strict-feedback systems[J]. Control Theory & Applications, 2021,38(5): 561-570.
[9] 李国军,王亢,卢甜甜,等.带有误差修正项的自适应学习控制[J].控制理论与应用,2021, 38(2):275-286.
LI G J, WANG K, LU T T. et.al. Adaptive learning control with rectifying error actions[J]. Control Theory & Applications, 2021, 38(2):275-286.
[10] 殷春武,甘婷,楚天乐等.基于时变终端滑模的任意迭代初态抑制控制[J].控制理论与应用, 2023, 40(6): 1105-1112.
YIN C W, GAN T, CHU T L, et.al. Arbitrary iterative initial value suppression control based on time-varying terminal sliding mode[J]. Control Theory & Applications, 2023, 40(6): 1105-1112.
[11] YIN C W, RIAZ S, et.al. A novel predefined time PD-Type ILC paradigm for nonlinear systems[J]. Mathematics, 2023 (11): 56.
[12] LIU B, ZHANG Z, WANG L, et al. Iterative learning tracking control of nonlinear multiagent systems with input saturation[J]. Complexity, 2021, (1):1-13.
[13] 邓雄峰,余磊,魏利胜等.输入受限下非完整轮式移动机器人迭代学习跟踪控制[J].电子测量与仪器学报, 2021, 35(9): 58-64.
DENG X F, YU L, WEI S L, et.al. Iterative learning tracking control of nonholonomic wheeled mobile robot with input constraint[J]. Journal of Electronic Measurement and Instrumentation, 2021,35(9):58-64. |
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